
After approximately solving (REF ), the DMD process computes the dominant spectral properties of the learned linear operator
[1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
The rank-\(r\)  constraint in (REF ) is motivated by the assumed modal structure of the system but doesn't account for other important physical properties.
For example, one limitation of DMD is that the solution of (REF ) lies within the span of \(Y\) , so the learned model rarely generalizes outside the training regime. We address this limitation and others by embedding partial physics knowledge into the learning process.
